Staff Software Engineer, AI/ML, YouTube Ads

Site Reliability Engineering (SRE) combines software and systems engineering to build and run large-scale, massively distributed, fault-tolerant systems. SRE ensures that Google Cloud's services—both our internally critical and our externally-visible systems—have reliability, uptime appropriate to customer's needs and a fast rate of improvement. Additionally SRE’s will keep an ever-watchful eye on our systems capacity and performance.

Much of our software development focuses on optimizing existing systems, building infrastructure and eliminating work through automation. On the SRE team, you’ll have the opportunity to manage the complex challenges of scale which are unique to Google Cloud, while using your expertise in coding, algorithms, complexity analysis and large-scale system design. SRE's culture of intellectual curiosity, problem solving and openness is key to its success. Our organization brings together people with a wide variety of backgrounds, experiences and perspectives. We encourage them to collaborate, think big and take risks in a blame-free environment. We promote self-direction to work on meaningful projects, while we also strive to create an environment that provides the support and mentorship needed to learn and grow.

Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

Ireland: €122000 - €126000 (EUR) + 15% bonus target + equity + benefits

Learn more about benefits at Google. Responsibilities

• Lead the end-to-end development of novel machine learning models, incorporating techniques like deep learning, reinforcement learning, and generative artificial intelligence, from concept to production.

• Build and scale end-to-end machine earning pipelines for model training, inference, and integration with high-throughput Ad serving systems.

• Explore, implement, and integrate with generative models for text, image, and video adaptations within Ads.

• Apply deep learning and reinforcement learning to understand asset value and optimize creative composition while developing metrics and algorithms to ensure creative freshness and efficient exploration.

• Contribute to the architecture of centralized services for unifying asset attributes and model-driven insights across different applications, collaborating with infrastructure and serving teams to power creative optimization.

Qualifications Minimum qualifications:

• Bachelor's degree or equivalent practical experience.

• 8 years of experience in software development.

• 5 years of experience testing and launching software products, and 3 years of experience with software design and architecture.

Preferred qualifications:

• Master's degree or PhD in Computer Science, Machine Learning, or a related technical field.

• 8 years of experience in data structures and algorithms.

• 5 years of experience building and productionizing machine learning models, with experience in deep learning or reinforcement learning.

• Experience with machine learning frameworks like TensorFlow and JAX, production machine learning platforms such as TensorFlow Extended and AdBrain, and generative models and their applications.

• Proficiency in designing, running, and analyzing large-scale online experiments (A/B tests).

• Familiarity with online advertising systems, creative optimization, personalization, or recommender systems.